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\begin{document}
\title{The Proposal to Improve the Scalability of Integer Linear Programming with Modular Performance Analysis}
\author{Shin-haeng Kang\\
Seoul National University}
\maketitle

\section{Introduction}
\EPSFIG[width=30mm]{ivsssystem.eps}{Current IVSS system performance estimation flow.}{FIG-FRAMEWORK}
Codesign and Parallel processing Labatory, CAPLAB, is developing ILP-based performance estimation framework with Hyundai Motors from Summer, 2010. Figure \REF{FIG-FRAMEWORK} presents the current flow of the performance estimation, specially the end-to-end delay estimation. Basically, ILP gives us very tight bound of WCRT but it takes too much analysis time since it is needed to search the space exhaustively. One solution to make ILP more scalable than pure ILP is to use simple min-max heuristic. As can be implied from the word  'simple', the heuristic does not provide enough tight bound. The execution time of ILP, however, is reduced significantly according to Kim's DAC paper. This result implies that setting up more bounds definetely affects the performance of ILP solver. 

\EPSFIG[width=80mm]{solutionspace.eps}{Solution Space.}{FIG-SOLUTIONSPACE}

As you can notice from Figure \REF{FIG-SOLUTIONSPACE}, It is assumed that MPA's solution area is bigger than ILP's solution area. In other words, MPA's solution is more generous/pessimistic. 

\section{Risk}
As Dr.Yang mensioned, if MPA can prune out only the trival space of the search space of ILP, MPA cannot contribute to reduce the execution time of ILP. 

\section{Feasibility Test}
I just provide ILP solver with the pessimistic WCRT which is given by MPA. And then I watch what it happens.


\section{Another potential approach}
\subsection{Georgia's idea}
...
\section{More...}

\section{Conclusion}

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